Search results

Purpose – This study examines both the short- and long-term share price reaction to announcements of financial restatements cited in the U.S. General Accounting Office (2006) database.

Methodology – It uses the augmented four-factor Fama-French model for assessing share price reaction.

Findings – The study finds that the average cumulative abnormal return (CAR) for a sample of 553 restatements (by 437 companies) is significantly negative (−1.58) for the three-day window surrounding the day of announcement. The average CAR for the one-year period prior to the announcement (−9.6%) and for each of the four years after the announcement is negative as well, with the average CAR for the four years adding up to −22%. The study also documents differences in CARs based on the entity prompting the restatement (company, auditor, and Securities and Exchange Commission), the reason behind the restatement (revenue, cost, reclassification of item, etc.), and for one-time versus repeat offenders.

Social implications – Taken together, the findings indicate that financial restatements impose significant short-term as well as long-term costs on shareholders.

Originality/Value – The evidence about long-term share price reaction to financial restatements is missing in prior research. The relationship between long-term and short-term share price reaction to financial restatements fails to suggest systematic over/underreaction by the market.

Industry 4.0 has been identified as a key cornerstone to modernise economies where man and machines complement each other seamlessly to achieve synergies in decision-making and productivity for contributing to SDG 8: Decent Work and Economic Growth and SDG 9: Industry, Innovation and Infrastructure. The integration of Industry 4.0 remains a challenge for the developing world, depending on their current status in the industrial revolution journey from its predecessors 1.0, 2.0 and 3.0. This chapter reviews reported findings in literature to highlight how robotics and automated systems can pave the way to implementing and applying the principles of Industry 4.0 for developing countries like Mauritius, where data collection, processing and analysis for decision-making and prediction are key components to be integrated or designed into industrial processes centred heavily on the use of artificial intelligence (AI) and machine learning techniques. Robotics has not yet found its way into the various industrial sectors in Mauritius, although it has been an important driver for Industry 4.0 across the world. The inherent barriers and transformations needed as well as the potential application scenarios are discussed.

Purpose: The main aim of this study is to explore the emergence and significance of blockchain technology in the financial system. This analysis examines the early stages of the adoption of blockchain technology.

Need of the study: To ascertain the viability of blockchain systems as a viable, fair, and traceable way of storing transaction records in Indian banking and financial services organisations.

Methodology: By virtue, this study is exploratory, following access to related studies on the implementation and applications of blockchain technology in the financial sector.

Findings: This study explores blockchain technology, its adoption, types, usefulness, benefits, challenges, and security concern in the banking sector.

Implications: This study will contribute to future research on applications of blockchain technology in the financial sector. It will help the researcher understand this technology’s importance and complications in the financial system.